Overview

Dataset statistics

Number of variables27
Number of observations50000
Missing cells30053
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 MiB
Average record size in memory216.0 B

Variable types

Text14
Categorical5
Numeric8

Alerts

Num_Bank_Accounts is highly overall correlated with Interest_Rate and 1 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Num_Bank_Accounts and 2 other fieldsHigh correlation
Delay_from_due_date is highly overall correlated with Num_Bank_Accounts and 1 other fieldsHigh correlation
Num_Credit_Inquiries is highly overall correlated with Interest_RateHigh correlation
Name has 5015 (10.0%) missing valuesMissing
Monthly_Inhand_Salary has 7498 (15.0%) missing valuesMissing
Type_of_Loan has 5704 (11.4%) missing valuesMissing
Num_of_Delayed_Payment has 3498 (7.0%) missing valuesMissing
Num_Credit_Inquiries has 1035 (2.1%) missing valuesMissing
Credit_History_Age has 4470 (8.9%) missing valuesMissing
Amount_invested_monthly has 2271 (4.5%) missing valuesMissing
Monthly_Balance has 562 (1.1%) missing valuesMissing
Month is uniformly distributedUniform
ID has unique valuesUnique
Credit_Utilization_Ratio has unique valuesUnique
Num_Bank_Accounts has 2166 (4.3%) zerosZeros
Delay_from_due_date has 626 (1.3%) zerosZeros
Num_Credit_Inquiries has 1102 (2.2%) zerosZeros
Total_EMI_per_month has 5002 (10.0%) zerosZeros

Reproduction

Analysis started2023-10-21 16:38:10.381848
Analysis finished2023-10-21 16:38:33.496310
Duration23.11 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

ID
Text

UNIQUE 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:33.854270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.60068
Min length6

Characters and Unicode

Total characters330034
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50000 ?
Unique (%)100.0%

Sample

1st row0x160a
2nd row0x160b
3rd row0x160c
4th row0x160d
5th row0x1616
ValueCountFrequency (%)
0x160a 1
 
< 0.1%
0x1691 1
 
< 0.1%
0x1649 1
 
< 0.1%
0x1625 1
 
< 0.1%
0x160c 1
 
< 0.1%
0x160d 1
 
< 0.1%
0x1616 1
 
< 0.1%
0x1617 1
 
< 0.1%
0x1618 1
 
< 0.1%
0x1619 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
2023-10-21T13:38:34.498332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 62051
18.8%
x 50000
15.1%
1 34749
10.5%
2 21607
 
6.5%
3 13419
 
4.1%
4 13417
 
4.1%
5 13415
 
4.1%
9 12141
 
3.7%
c 12141
 
3.7%
e 12139
 
3.7%
Other values (7) 84955
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 207212
62.8%
Lowercase Letter 122822
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62051
29.9%
1 34749
16.8%
2 21607
 
10.4%
3 13419
 
6.5%
4 13417
 
6.5%
5 13415
 
6.5%
9 12141
 
5.9%
8 12139
 
5.9%
6 12139
 
5.9%
7 12135
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
x 50000
40.7%
c 12141
 
9.9%
e 12139
 
9.9%
b 12139
 
9.9%
d 12135
 
9.9%
a 12135
 
9.9%
f 12133
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 207212
62.8%
Latin 122822
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62051
29.9%
1 34749
16.8%
2 21607
 
10.4%
3 13419
 
6.5%
4 13417
 
6.5%
5 13415
 
6.5%
9 12141
 
5.9%
8 12139
 
5.9%
6 12139
 
5.9%
7 12135
 
5.9%
Latin
ValueCountFrequency (%)
x 50000
40.7%
c 12141
 
9.9%
e 12139
 
9.9%
b 12139
 
9.9%
d 12135
 
9.9%
a 12135
 
9.9%
f 12133
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62051
18.8%
x 50000
15.1%
1 34749
10.5%
2 21607
 
6.5%
3 13419
 
4.1%
4 13417
 
4.1%
5 13415
 
4.1%
9 12141
 
3.7%
c 12141
 
3.7%
e 12139
 
3.7%
Other values (7) 84955
25.7%
Distinct12500
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:34.916352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.93952
Min length9

Characters and Unicode

Total characters496976
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUS_0xd40
2nd rowCUS_0xd40
3rd rowCUS_0xd40
4th rowCUS_0xd40
5th rowCUS_0x21b1
ValueCountFrequency (%)
cus_0xd40 4
 
< 0.1%
cus_0x75c6 4
 
< 0.1%
cus_0x5b48 4
 
< 0.1%
cus_0xc0ab 4
 
< 0.1%
cus_0x2dbc 4
 
< 0.1%
cus_0xb891 4
 
< 0.1%
cus_0x1cdb 4
 
< 0.1%
cus_0x95ee 4
 
< 0.1%
cus_0x284a 4
 
< 0.1%
cus_0x5407 4
 
< 0.1%
Other values (12490) 49960
99.9%
2023-10-21T13:38:35.629983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59124
11.9%
C 50000
 
10.1%
S 50000
 
10.1%
_ 50000
 
10.1%
x 50000
 
10.1%
U 50000
 
10.1%
4 14000
 
2.8%
6 13700
 
2.8%
5 13600
 
2.7%
3 13536
 
2.7%
Other values (11) 133016
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180604
36.3%
Uppercase Letter 150000
30.2%
Lowercase Letter 116372
23.4%
Connector Punctuation 50000
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59124
32.7%
4 14000
 
7.8%
6 13700
 
7.6%
5 13600
 
7.5%
3 13536
 
7.5%
8 13536
 
7.5%
7 13388
 
7.4%
9 13368
 
7.4%
2 13360
 
7.4%
1 12992
 
7.2%
Lowercase Letter
ValueCountFrequency (%)
x 50000
43.0%
b 13400
 
11.5%
a 13272
 
11.4%
c 11136
 
9.6%
e 9744
 
8.4%
d 9436
 
8.1%
f 9384
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
C 50000
33.3%
S 50000
33.3%
U 50000
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 266372
53.6%
Common 230604
46.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59124
25.6%
_ 50000
21.7%
4 14000
 
6.1%
6 13700
 
5.9%
5 13600
 
5.9%
3 13536
 
5.9%
8 13536
 
5.9%
7 13388
 
5.8%
9 13368
 
5.8%
2 13360
 
5.8%
Latin
ValueCountFrequency (%)
C 50000
18.8%
S 50000
18.8%
x 50000
18.8%
U 50000
18.8%
b 13400
 
5.0%
a 13272
 
5.0%
c 11136
 
4.2%
e 9744
 
3.7%
d 9436
 
3.5%
f 9384
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59124
11.9%
C 50000
 
10.1%
S 50000
 
10.1%
_ 50000
 
10.1%
x 50000
 
10.1%
U 50000
 
10.1%
4 14000
 
2.8%
6 13700
 
2.8%
5 13600
 
2.7%
3 13536
 
2.7%
Other values (11) 133016
26.8%

Month
Categorical

UNIFORM 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
September
12500 
October
12500 
November
12500 
December
12500 

Length

Max length9
Median length8.5
Mean length8
Min length7

Characters and Unicode

Total characters400000
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeptember
2nd rowOctober
3rd rowNovember
4th rowDecember
5th rowSeptember

Common Values

ValueCountFrequency (%)
September 12500
25.0%
October 12500
25.0%
November 12500
25.0%
December 12500
25.0%

Length

2023-10-21T13:38:35.885377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T13:38:36.207162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
september 12500
25.0%
october 12500
25.0%
november 12500
25.0%
december 12500
25.0%

Most occurring characters

ValueCountFrequency (%)
e 112500
28.1%
b 50000
12.5%
r 50000
12.5%
m 37500
 
9.4%
t 25000
 
6.2%
c 25000
 
6.2%
o 25000
 
6.2%
S 12500
 
3.1%
p 12500
 
3.1%
O 12500
 
3.1%
Other values (3) 37500
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 350000
87.5%
Uppercase Letter 50000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 112500
32.1%
b 50000
14.3%
r 50000
14.3%
m 37500
 
10.7%
t 25000
 
7.1%
c 25000
 
7.1%
o 25000
 
7.1%
p 12500
 
3.6%
v 12500
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S 12500
25.0%
O 12500
25.0%
N 12500
25.0%
D 12500
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 400000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 112500
28.1%
b 50000
12.5%
r 50000
12.5%
m 37500
 
9.4%
t 25000
 
6.2%
c 25000
 
6.2%
o 25000
 
6.2%
S 12500
 
3.1%
p 12500
 
3.1%
O 12500
 
3.1%
Other values (3) 37500
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 112500
28.1%
b 50000
12.5%
r 50000
12.5%
m 37500
 
9.4%
t 25000
 
6.2%
c 25000
 
6.2%
o 25000
 
6.2%
S 12500
 
3.1%
p 12500
 
3.1%
O 12500
 
3.1%
Other values (3) 37500
 
9.4%

Name
Text

MISSING 

Distinct10139
Distinct (%)22.5%
Missing5015
Missing (%)10.0%
Memory size390.8 KiB
2023-10-21T13:38:36.675480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length25
Median length20
Mean length9.758275
Min length2

Characters and Unicode

Total characters438976
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.1%

Sample

1st rowAaron Maashoh
2nd rowAaron Maashoh
3rd rowAaron Maashoh
4th rowAaron Maashoh
5th rowRick Rothackerj
ValueCountFrequency (%)
david 328
 
0.5%
jonathan 300
 
0.5%
jessica 256
 
0.4%
sarah 212
 
0.3%
karen 190
 
0.3%
nick 184
 
0.3%
tim 184
 
0.3%
caroline 181
 
0.3%
tom 174
 
0.3%
john 169
 
0.3%
Other values (9720) 60616
96.5%
2023-10-21T13:38:37.481718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 45799
 
10.4%
e 38101
 
8.7%
n 29539
 
6.7%
i 29158
 
6.6%
r 27144
 
6.2%
o 22261
 
5.1%
l 21086
 
4.8%
17825
 
4.1%
t 17446
 
4.0%
h 15228
 
3.5%
Other values (47) 175389
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 356743
81.3%
Uppercase Letter 62618
 
14.3%
Space Separator 17825
 
4.1%
Other Punctuation 1075
 
0.2%
Dash Punctuation 715
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 45799
12.8%
e 38101
 
10.7%
n 29539
 
8.3%
i 29158
 
8.2%
r 27144
 
7.6%
o 22261
 
6.2%
l 21086
 
5.9%
t 17446
 
4.9%
h 15228
 
4.3%
s 15226
 
4.3%
Other values (16) 95755
26.8%
Uppercase Letter
ValueCountFrequency (%)
S 7100
 
11.3%
A 4370
 
7.0%
M 4314
 
6.9%
L 4206
 
6.7%
J 4014
 
6.4%
C 3883
 
6.2%
R 3587
 
5.7%
D 3511
 
5.6%
K 3447
 
5.5%
B 3251
 
5.2%
Other values (16) 20935
33.4%
Other Punctuation
ValueCountFrequency (%)
. 551
51.3%
" 478
44.5%
, 46
 
4.3%
Space Separator
ValueCountFrequency (%)
17825
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 419361
95.5%
Common 19615
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 45799
 
10.9%
e 38101
 
9.1%
n 29539
 
7.0%
i 29158
 
7.0%
r 27144
 
6.5%
o 22261
 
5.3%
l 21086
 
5.0%
t 17446
 
4.2%
h 15228
 
3.6%
s 15226
 
3.6%
Other values (42) 158373
37.8%
Common
ValueCountFrequency (%)
17825
90.9%
- 715
 
3.6%
. 551
 
2.8%
" 478
 
2.4%
, 46
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 438976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 45799
 
10.4%
e 38101
 
8.7%
n 29539
 
6.7%
i 29158
 
6.6%
r 27144
 
6.2%
o 22261
 
5.1%
l 21086
 
4.8%
17825
 
4.1%
t 17446
 
4.0%
h 15228
 
3.5%
Other values (47) 175389
40.0%

Age
Text

Distinct976
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:37.867441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.10342
Min length2

Characters and Unicode

Total characters105171
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839 ?
Unique (%)1.7%

Sample

1st row23
2nd row24
3rd row24
4th row24_
5th row28
ValueCountFrequency (%)
39 1570
 
3.1%
32 1529
 
3.1%
44 1500
 
3.0%
22 1493
 
3.0%
35 1483
 
3.0%
37 1461
 
2.9%
27 1457
 
2.9%
29 1441
 
2.9%
20 1432
 
2.9%
26 1421
 
2.8%
Other values (918) 35213
70.4%
2023-10-21T13:38:38.390459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 19446
18.5%
3 19144
18.2%
4 16616
15.8%
5 10984
10.4%
1 9785
9.3%
0 5994
 
5.7%
6 5690
 
5.4%
9 5305
 
5.0%
7 4704
 
4.5%
8 4562
 
4.3%
Other values (2) 2941
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102230
97.2%
Connector Punctuation 2477
 
2.4%
Dash Punctuation 464
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19446
19.0%
3 19144
18.7%
4 16616
16.3%
5 10984
10.7%
1 9785
9.6%
0 5994
 
5.9%
6 5690
 
5.6%
9 5305
 
5.2%
7 4704
 
4.6%
8 4562
 
4.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2477
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19446
18.5%
3 19144
18.2%
4 16616
15.8%
5 10984
10.4%
1 9785
9.3%
0 5994
 
5.7%
6 5690
 
5.4%
9 5305
 
5.0%
7 4704
 
4.5%
8 4562
 
4.3%
Other values (2) 2941
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19446
18.5%
3 19144
18.2%
4 16616
15.8%
5 10984
10.4%
1 9785
9.3%
0 5994
 
5.7%
6 5690
 
5.4%
9 5305
 
5.0%
7 4704
 
4.5%
8 4562
 
4.3%
Other values (2) 2941
 
2.8%

SSN
Text

Distinct12501
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:38.799866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.88688
Min length9

Characters and Unicode

Total characters544344
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row821-00-0265
2nd row821-00-0265
3rd row821-00-0265
4th row821-00-0265
5th row004-07-5839
ValueCountFrequency (%)
f%$d@*&8 2828
 
5.7%
601-99-6960 4
 
< 0.1%
242-49-5854 4
 
< 0.1%
470-60-1304 4
 
< 0.1%
818-94-9241 4
 
< 0.1%
180-42-9738 4
 
< 0.1%
600-70-6095 4
 
< 0.1%
098-21-5869 4
 
< 0.1%
039-89-0333 4
 
< 0.1%
266-96-2968 4
 
< 0.1%
Other values (12491) 47136
94.3%
2023-10-21T13:38:39.399319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 94344
17.3%
8 45742
8.4%
1 43235
7.9%
4 42874
7.9%
2 42687
7.8%
7 42591
7.8%
9 42261
7.8%
0 42219
7.8%
5 42183
7.7%
3 41862
7.7%
Other values (9) 64346
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 427376
78.5%
Dash Punctuation 94344
 
17.3%
Other Punctuation 14140
 
2.6%
Uppercase Letter 5656
 
1.0%
Currency Symbol 2828
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 45742
10.7%
1 43235
10.1%
4 42874
10.0%
2 42687
10.0%
7 42591
10.0%
9 42261
9.9%
0 42219
9.9%
5 42183
9.9%
3 41862
9.8%
6 41722
9.8%
Other Punctuation
ValueCountFrequency (%)
& 2828
20.0%
* 2828
20.0%
@ 2828
20.0%
% 2828
20.0%
# 2828
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 2828
50.0%
D 2828
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 94344
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 538688
99.0%
Latin 5656
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 94344
17.5%
8 45742
8.5%
1 43235
8.0%
4 42874
8.0%
2 42687
7.9%
7 42591
7.9%
9 42261
7.8%
0 42219
7.8%
5 42183
7.8%
3 41862
7.8%
Other values (7) 58690
10.9%
Latin
ValueCountFrequency (%)
F 2828
50.0%
D 2828
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 544344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 94344
17.3%
8 45742
8.4%
1 43235
7.9%
4 42874
7.9%
2 42687
7.8%
7 42591
7.8%
9 42261
7.8%
0 42219
7.8%
5 42183
7.7%
3 41862
7.7%
Other values (9) 64346
11.8%

Occupation
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
_______
3438 
Lawyer
 
3324
Engineer
 
3212
Architect
 
3195
Mechanic
 
3168
Other values (11)
33663 

Length

Max length13
Median length10
Mean length8.43476
Min length6

Characters and Unicode

Total characters421738
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScientist
2nd rowScientist
3rd rowScientist
4th rowScientist
5th row_______

Common Values

ValueCountFrequency (%)
_______ 3438
 
6.9%
Lawyer 3324
 
6.6%
Engineer 3212
 
6.4%
Architect 3195
 
6.4%
Mechanic 3168
 
6.3%
Developer 3146
 
6.3%
Accountant 3133
 
6.3%
Media_Manager 3130
 
6.3%
Scientist 3104
 
6.2%
Teacher 3103
 
6.2%
Other values (6) 18047
36.1%

Length

2023-10-21T13:38:39.675930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3438
 
6.9%
lawyer 3324
 
6.6%
engineer 3212
 
6.4%
architect 3195
 
6.4%
mechanic 3168
 
6.3%
developer 3146
 
6.3%
accountant 3133
 
6.3%
media_manager 3130
 
6.3%
scientist 3104
 
6.2%
teacher 3103
 
6.2%
Other values (6) 18047
36.1%

Most occurring characters

ValueCountFrequency (%)
e 56361
13.4%
r 43349
10.3%
n 37282
 
8.8%
a 34102
 
8.1%
c 31173
 
7.4%
t 30964
 
7.3%
i 30777
 
7.3%
_ 27196
 
6.4%
M 15375
 
3.6%
o 15370
 
3.6%
Other values (18) 99789
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 344850
81.8%
Uppercase Letter 49692
 
11.8%
Connector Punctuation 27196
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 56361
16.3%
r 43349
12.6%
n 37282
10.8%
a 34102
9.9%
c 31173
9.0%
t 30964
9.0%
i 30777
8.9%
o 15370
 
4.5%
u 12220
 
3.5%
h 9466
 
2.7%
Other values (8) 43786
12.7%
Uppercase Letter
ValueCountFrequency (%)
M 15375
30.9%
A 6328
12.7%
E 6315
12.7%
D 6173
12.4%
L 3324
 
6.7%
S 3104
 
6.2%
T 3103
 
6.2%
J 3037
 
6.1%
W 2933
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 27196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 394542
93.6%
Common 27196
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 56361
14.3%
r 43349
11.0%
n 37282
9.4%
a 34102
 
8.6%
c 31173
 
7.9%
t 30964
 
7.8%
i 30777
 
7.8%
M 15375
 
3.9%
o 15370
 
3.9%
u 12220
 
3.1%
Other values (17) 87569
22.2%
Common
ValueCountFrequency (%)
_ 27196
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 421738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 56361
13.4%
r 43349
10.3%
n 37282
 
8.8%
a 34102
 
8.1%
c 31173
 
7.4%
t 30964
 
7.3%
i 30777
 
7.3%
_ 27196
 
6.4%
M 15375
 
3.6%
o 15370
 
3.6%
Other values (18) 99789
23.7%
Distinct16121
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:40.100838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length19
Median length8
Mean length8.3094
Min length6

Characters and Unicode

Total characters415470
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3335 ?
Unique (%)6.7%

Sample

1st row19114.12
2nd row19114.12
3rd row19114.12
4th row19114.12
5th row34847.84
ValueCountFrequency (%)
109945.32 8
 
< 0.1%
20867.67 8
 
< 0.1%
17816.75 8
 
< 0.1%
32543.38 8
 
< 0.1%
40341.16 8
 
< 0.1%
33029.66 8
 
< 0.1%
17273.83 8
 
< 0.1%
22434.16 8
 
< 0.1%
36585.12 8
 
< 0.1%
9141.63 8
 
< 0.1%
Other values (12979) 49920
99.8%
2023-10-21T13:38:40.967898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 50000
12.0%
1 46376
11.2%
2 38186
9.2%
4 35878
8.6%
3 35800
8.6%
8 35462
8.5%
5 35357
8.5%
6 35326
8.5%
9 34524
8.3%
0 33173
8.0%
Other values (2) 35388
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 361950
87.1%
Other Punctuation 50000
 
12.0%
Connector Punctuation 3520
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 46376
12.8%
2 38186
10.6%
4 35878
9.9%
3 35800
9.9%
8 35462
9.8%
5 35357
9.8%
6 35326
9.8%
9 34524
9.5%
0 33173
9.2%
7 31868
8.8%
Other Punctuation
ValueCountFrequency (%)
. 50000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3520
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 415470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 50000
12.0%
1 46376
11.2%
2 38186
9.2%
4 35878
8.6%
3 35800
8.6%
8 35462
8.5%
5 35357
8.5%
6 35326
8.5%
9 34524
8.3%
0 33173
8.0%
Other values (2) 35388
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 415470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 50000
12.0%
1 46376
11.2%
2 38186
9.2%
4 35878
8.6%
3 35800
8.6%
8 35462
8.5%
5 35357
8.5%
6 35326
8.5%
9 34524
8.3%
0 33173
8.0%
Other values (2) 35388
8.5%

Monthly_Inhand_Salary
Real number (ℝ)

MISSING 

Distinct12793
Distinct (%)30.1%
Missing7498
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean4182.0043
Minimum303.64542
Maximum15204.633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:41.214820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum303.64542
5-th percentile833.77315
Q11625.1883
median3086.305
Q35934.1891
95-th percentile10771.233
Maximum15204.633
Range14900.988
Interquartile range (IQR)4309.0008

Descriptive statistics

Standard deviation3174.1093
Coefficient of variation (CV)0.75899236
Kurtosis0.62806109
Mean4182.0043
Median Absolute Deviation (MAD)1750.8296
Skewness1.131374
Sum1.7774355 × 108
Variance10074970
MonotonicityNot monotonic
2023-10-21T13:38:41.458050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1315.560833 8
 
< 0.1%
3080.555 7
 
< 0.1%
2295.058333 7
 
< 0.1%
6082.1875 7
 
< 0.1%
4387.2725 7
 
< 0.1%
536.43125 7
 
< 0.1%
6639.56 7
 
< 0.1%
5766.491667 7
 
< 0.1%
6358.956667 6
 
< 0.1%
1762.895 4
 
< 0.1%
Other values (12783) 42435
84.9%
(Missing) 7498
 
15.0%
ValueCountFrequency (%)
303.6454167 2
< 0.1%
319.55625 4
< 0.1%
331.0319233 2
< 0.1%
332.1283333 3
< 0.1%
332.43125 4
< 0.1%
333.5966667 4
< 0.1%
355.2083333 4
< 0.1%
357.2558333 4
< 0.1%
358.0583333 4
< 0.1%
361.6033333 4
< 0.1%
ValueCountFrequency (%)
15204.63333 3
< 0.1%
15167.18 4
< 0.1%
15136.69667 3
< 0.1%
15115.19 3
< 0.1%
15101.94 3
< 0.1%
15090.07667 4
< 0.1%
15066.78333 4
< 0.1%
14978.33667 3
< 0.1%
14960.25 1
 
< 0.1%
14929.54 3
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct540
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.83826
Minimum-1
Maximum1798
Zeros2166
Zeros (%)4.3%
Negative16
Negative (%)< 0.1%
Memory size390.8 KiB
2023-10-21T13:38:41.744884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median6
Q37
95-th percentile10
Maximum1798
Range1799
Interquartile range (IQR)4

Descriptive statistics

Standard deviation116.39685
Coefficient of variation (CV)6.9126411
Kurtosis132.91918
Mean16.83826
Median Absolute Deviation (MAD)2
Skewness11.251682
Sum841913
Variance13548.226
MonotonicityNot monotonic
2023-10-21T13:38:42.000287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 6504
13.0%
7 6408
12.8%
8 6387
12.8%
4 6100
12.2%
5 6068
12.1%
3 5955
11.9%
9 2738
5.5%
10 2599
 
5.2%
1 2253
 
4.5%
0 2166
 
4.3%
Other values (530) 2822
5.6%
ValueCountFrequency (%)
-1 16
 
< 0.1%
0 2166
 
4.3%
1 2253
 
4.5%
2 2152
 
4.3%
3 5955
11.9%
4 6100
12.2%
5 6068
12.1%
6 6504
13.0%
7 6408
12.8%
8 6387
12.8%
ValueCountFrequency (%)
1798 1
< 0.1%
1783 1
< 0.1%
1781 1
< 0.1%
1780 1
< 0.1%
1775 1
< 0.1%
1774 2
< 0.1%
1773 1
< 0.1%
1772 1
< 0.1%
1771 1
< 0.1%
1770 1
< 0.1%

Num_Credit_Card
Real number (ℝ)

Distinct819
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.92148
Minimum0
Maximum1499
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:42.221179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q37
95-th percentile10
Maximum1499
Range1499
Interquartile range (IQR)3

Descriptive statistics

Standard deviation129.3148
Coefficient of variation (CV)5.6416429
Kurtosis71.870659
Mean22.92148
Median Absolute Deviation (MAD)2
Skewness8.2868797
Sum1146074
Variance16722.319
MonotonicityNot monotonic
2023-10-21T13:38:42.449218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 9210
18.4%
7 8271
16.5%
6 8243
16.5%
4 7072
14.1%
3 6539
13.1%
8 2497
 
5.0%
10 2405
 
4.8%
9 2333
 
4.7%
2 1131
 
2.3%
1 1063
 
2.1%
Other values (809) 1236
 
2.5%
ValueCountFrequency (%)
0 16
 
< 0.1%
1 1063
 
2.1%
2 1131
 
2.3%
3 6539
13.1%
4 7072
14.1%
5 9210
18.4%
6 8243
16.5%
7 8271
16.5%
8 2497
 
5.0%
9 2333
 
4.7%
ValueCountFrequency (%)
1499 1
 
< 0.1%
1498 2
< 0.1%
1495 1
 
< 0.1%
1491 1
 
< 0.1%
1488 1
 
< 0.1%
1486 1
 
< 0.1%
1485 1
 
< 0.1%
1484 2
< 0.1%
1481 2
< 0.1%
1474 3
< 0.1%

Interest_Rate
Real number (ℝ)

HIGH CORRELATION 

Distinct945
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.77264
Minimum1
Maximum5799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:42.681764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median13
Q320
95-th percentile32
Maximum5799
Range5798
Interquartile range (IQR)12

Descriptive statistics

Standard deviation451.60236
Coefficient of variation (CV)6.5665992
Kurtosis92.486565
Mean68.77264
Median Absolute Deviation (MAD)6
Skewness9.3702231
Sum3438632
Variance203944.69
MonotonicityNot monotonic
2023-10-21T13:38:42.908397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 2503
 
5.0%
5 2500
 
5.0%
6 2368
 
4.7%
12 2288
 
4.6%
10 2259
 
4.5%
9 2253
 
4.5%
7 2250
 
4.5%
11 2198
 
4.4%
18 2052
 
4.1%
15 1992
 
4.0%
Other values (935) 27337
54.7%
ValueCountFrequency (%)
1 1344
2.7%
2 1245
2.5%
3 1388
2.8%
4 1287
2.6%
5 2500
5.0%
6 2368
4.7%
7 2250
4.5%
8 2503
5.0%
9 2253
4.5%
10 2259
4.5%
ValueCountFrequency (%)
5799 1
< 0.1%
5792 1
< 0.1%
5773 1
< 0.1%
5759 2
< 0.1%
5752 1
< 0.1%
5748 1
< 0.1%
5747 1
< 0.1%
5743 1
< 0.1%
5736 1
< 0.1%
5732 1
< 0.1%
Distinct263
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:43.330365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.17906
Min length1

Characters and Unicode

Total characters58953
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)0.5%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row1
ValueCountFrequency (%)
2 7515
15.0%
3 7514
15.0%
4 7368
14.7%
0 5446
10.9%
1 5295
10.6%
6 3902
7.8%
7 3680
7.4%
5 3617
7.2%
100 1974
 
3.9%
9 1837
 
3.7%
Other values (242) 1852
 
3.7%
2023-10-21T13:38:43.989491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9458
16.0%
2 7597
12.9%
3 7597
12.9%
4 7476
12.7%
1 7438
12.6%
6 3976
6.7%
7 3742
 
6.3%
5 3696
 
6.3%
_ 2436
 
4.1%
- 1974
 
3.3%
Other values (2) 3563
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54543
92.5%
Connector Punctuation 2436
 
4.1%
Dash Punctuation 1974
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9458
17.3%
2 7597
13.9%
3 7597
13.9%
4 7476
13.7%
1 7438
13.6%
6 3976
7.3%
7 3742
 
6.9%
5 3696
 
6.8%
9 1908
 
3.5%
8 1655
 
3.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2436
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9458
16.0%
2 7597
12.9%
3 7597
12.9%
4 7476
12.7%
1 7438
12.6%
6 3976
6.7%
7 3742
 
6.3%
5 3696
 
6.3%
_ 2436
 
4.1%
- 1974
 
3.3%
Other values (2) 3563
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9458
16.0%
2 7597
12.9%
3 7597
12.9%
4 7476
12.7%
1 7438
12.6%
6 3976
6.7%
7 3742
 
6.3%
5 3696
 
6.3%
_ 2436
 
4.1%
- 1974
 
3.3%
Other values (2) 3563
 
6.0%

Type_of_Loan
Text

MISSING 

Distinct6260
Distinct (%)14.1%
Missing5704
Missing (%)11.4%
Memory size390.8 KiB
2023-10-21T13:38:44.244612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length182
Median length142
Mean length66.683583
Min length9

Characters and Unicode

Total characters2953816
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
2nd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
3rd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
4th rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
5th rowCredit-Builder Loan
ValueCountFrequency (%)
loan 156836
36.4%
and 38732
 
9.0%
payday 20284
 
4.7%
credit-builder 20220
 
4.7%
not 19808
 
4.6%
specified 19808
 
4.6%
home 19552
 
4.5%
equity 19552
 
4.5%
student 19484
 
4.5%
mortgage 19468
 
4.5%
Other values (4) 77216
17.9%
2023-10-21T13:38:44.706924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
386664
13.1%
o 312268
10.6%
a 294436
 
10.0%
n 273272
 
9.3%
e 177392
 
6.0%
t 175788
 
6.0%
d 158136
 
5.4%
L 156836
 
5.3%
i 138384
 
4.7%
, 132348
 
4.5%
Other values (23) 748292
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2002136
67.8%
Uppercase Letter 412448
 
14.0%
Space Separator 386664
 
13.1%
Other Punctuation 132348
 
4.5%
Dash Punctuation 20220
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 312268
15.6%
a 294436
14.7%
n 273272
13.6%
e 177392
8.9%
t 175788
8.8%
d 158136
7.9%
i 138384
6.9%
r 79352
 
4.0%
u 78252
 
3.9%
y 60120
 
3.0%
Other values (9) 254736
12.7%
Uppercase Letter
ValueCountFrequency (%)
L 156836
38.0%
P 39728
 
9.6%
C 39608
 
9.6%
S 39292
 
9.5%
B 20220
 
4.9%
N 19808
 
4.8%
H 19552
 
4.7%
E 19552
 
4.7%
M 19468
 
4.7%
D 19388
 
4.7%
Space Separator
ValueCountFrequency (%)
386664
100.0%
Other Punctuation
ValueCountFrequency (%)
, 132348
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2414584
81.7%
Common 539232
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 312268
12.9%
a 294436
12.2%
n 273272
11.3%
e 177392
 
7.3%
t 175788
 
7.3%
d 158136
 
6.5%
L 156836
 
6.5%
i 138384
 
5.7%
r 79352
 
3.3%
u 78252
 
3.2%
Other values (20) 570468
23.6%
Common
ValueCountFrequency (%)
386664
71.7%
, 132348
 
24.5%
- 20220
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2953816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
386664
13.1%
o 312268
10.6%
a 294436
 
10.0%
n 273272
 
9.3%
e 177392
 
6.0%
t 175788
 
6.0%
d 158136
 
5.4%
L 156836
 
5.3%
i 138384
 
4.7%
, 132348
 
4.5%
Other values (23) 748292
25.3%

Delay_from_due_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.05264
Minimum-5
Maximum67
Zeros626
Zeros (%)1.3%
Negative298
Negative (%)0.6%
Memory size390.8 KiB
2023-10-21T13:38:44.943756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.860397
Coefficient of variation (CV)0.70586859
Kurtosis0.3444274
Mean21.05264
Median Absolute Deviation (MAD)9
Skewness0.96492811
Sum1052632
Variance220.83141
MonotonicityNot monotonic
2023-10-21T13:38:45.178129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 1761
 
3.5%
15 1759
 
3.5%
8 1680
 
3.4%
9 1656
 
3.3%
10 1645
 
3.3%
14 1636
 
3.3%
12 1625
 
3.2%
7 1587
 
3.2%
6 1584
 
3.2%
11 1573
 
3.1%
Other values (63) 33494
67.0%
ValueCountFrequency (%)
-5 18
 
< 0.1%
-4 49
 
0.1%
-3 59
 
0.1%
-2 71
 
0.1%
-1 101
 
0.2%
0 626
1.3%
1 668
1.3%
2 669
1.3%
3 848
1.7%
4 825
1.7%
ValueCountFrequency (%)
67 7
 
< 0.1%
66 12
 
< 0.1%
65 30
 
0.1%
64 33
 
0.1%
63 21
 
< 0.1%
62 279
0.6%
61 271
0.5%
60 259
0.5%
59 250
0.5%
58 282
0.6%
Distinct443
Distinct (%)1.0%
Missing3498
Missing (%)7.0%
Memory size390.8 KiB
2023-10-21T13:38:45.605655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.772913
Min length1

Characters and Unicode

Total characters82444
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique367 ?
Unique (%)0.8%

Sample

1st row7
2nd row9
3rd row4
4th row5
5th row1
ValueCountFrequency (%)
19 2707
 
5.8%
15 2674
 
5.8%
16 2637
 
5.7%
17 2636
 
5.7%
18 2631
 
5.7%
10 2591
 
5.6%
12 2563
 
5.5%
20 2518
 
5.4%
11 2504
 
5.4%
9 2440
 
5.2%
Other values (398) 20601
44.3%
2023-10-21T13:38:46.257328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 30118
36.5%
2 13020
15.8%
0 6032
 
7.3%
9 5262
 
6.4%
8 5260
 
6.4%
5 4698
 
5.7%
3 4317
 
5.2%
7 4033
 
4.9%
6 4015
 
4.9%
4 3975
 
4.8%
Other values (2) 1714
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80730
97.9%
Connector Punctuation 1427
 
1.7%
Dash Punctuation 287
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30118
37.3%
2 13020
16.1%
0 6032
 
7.5%
9 5262
 
6.5%
8 5260
 
6.5%
5 4698
 
5.8%
3 4317
 
5.3%
7 4033
 
5.0%
6 4015
 
5.0%
4 3975
 
4.9%
Connector Punctuation
ValueCountFrequency (%)
_ 1427
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82444
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30118
36.5%
2 13020
15.8%
0 6032
 
7.3%
9 5262
 
6.4%
8 5260
 
6.4%
5 4698
 
5.7%
3 4317
 
5.2%
7 4033
 
4.9%
6 4015
 
4.9%
4 3975
 
4.8%
Other values (2) 1714
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30118
36.5%
2 13020
15.8%
0 6032
 
7.3%
9 5262
 
6.4%
8 5260
 
6.4%
5 4698
 
5.7%
3 4317
 
5.2%
7 4033
 
4.9%
6 4015
 
4.9%
4 3975
 
4.8%
Other values (2) 1714
 
2.1%
Distinct3927
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:46.682656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length21
Median length20
Mean length4.69558
Min length1

Characters and Unicode

Total characters234779
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique695 ?
Unique (%)1.4%

Sample

1st row11.27
2nd row13.27
3rd row12.27
4th row11.27
5th row5.42
ValueCountFrequency (%)
1059
 
2.1%
11.5 70
 
0.1%
11.32 63
 
0.1%
7.01 60
 
0.1%
7.35 60
 
0.1%
10.06 57
 
0.1%
3.93 57
 
0.1%
7.63 56
 
0.1%
7.69 56
 
0.1%
8.22 56
 
0.1%
Other values (3471) 48406
96.8%
2023-10-21T13:38:47.374045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 48941
20.8%
1 34489
14.7%
9 23001
9.8%
0 19861
8.5%
2 18122
 
7.7%
7 15298
 
6.5%
8 15257
 
6.5%
5 14815
 
6.3%
6 14574
 
6.2%
3 14319
 
6.1%
Other values (3) 16102
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183944
78.3%
Other Punctuation 48941
 
20.8%
Connector Punctuation 1059
 
0.5%
Dash Punctuation 835
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34489
18.7%
9 23001
12.5%
0 19861
10.8%
2 18122
9.9%
7 15298
8.3%
8 15257
8.3%
5 14815
8.1%
6 14574
7.9%
3 14319
7.8%
4 14208
7.7%
Other Punctuation
ValueCountFrequency (%)
. 48941
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1059
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 835
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 234779
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 48941
20.8%
1 34489
14.7%
9 23001
9.8%
0 19861
8.5%
2 18122
 
7.7%
7 15298
 
6.5%
8 15257
 
6.5%
5 14815
 
6.3%
6 14574
 
6.2%
3 14319
 
6.1%
Other values (3) 16102
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 48941
20.8%
1 34489
14.7%
9 23001
9.8%
0 19861
8.5%
2 18122
 
7.7%
7 15298
 
6.5%
8 15257
 
6.5%
5 14815
 
6.3%
6 14574
 
6.2%
3 14319
 
6.1%
Other values (3) 16102
 
6.9%

Num_Credit_Inquiries
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct750
Distinct (%)1.5%
Missing1035
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean30.0802
Minimum0
Maximum2593
Zeros1102
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:47.639256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile15
Maximum2593
Range2593
Interquartile range (IQR)6

Descriptive statistics

Standard deviation196.98412
Coefficient of variation (CV)6.5486306
Kurtosis96.36986
Mean30.0802
Median Absolute Deviation (MAD)3
Skewness9.5871727
Sum1472877
Variance38802.744
MonotonicityNot monotonic
2023-10-21T13:38:47.872514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 4709
9.4%
4 4402
 
8.8%
6 4375
 
8.8%
7 4295
 
8.6%
8 3922
 
7.8%
9 3523
 
7.0%
3 3466
 
6.9%
11 2996
 
6.0%
10 2982
 
6.0%
12 2585
 
5.2%
Other values (740) 11710
23.4%
ValueCountFrequency (%)
0 1102
 
2.2%
1 1747
 
3.5%
2 2454
4.9%
3 3466
6.9%
4 4402
8.8%
5 4709
9.4%
6 4375
8.8%
7 4295
8.6%
8 3922
7.8%
9 3523
7.0%
ValueCountFrequency (%)
2593 1
< 0.1%
2592 1
< 0.1%
2588 1
< 0.1%
2586 1
< 0.1%
2583 1
< 0.1%
2576 1
< 0.1%
2575 1
< 0.1%
2574 1
< 0.1%
2570 1
< 0.1%
2567 1
< 0.1%

Credit_Mix
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Standard
18379 
Good
12260 
_
9805 
Bad
9556 

Length

Max length8
Median length4
Mean length4.6909
Min length1

Characters and Unicode

Total characters234545
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard 18379
36.8%
Good 12260
24.5%
_ 9805
19.6%
Bad 9556
19.1%

Length

2023-10-21T13:38:48.089390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T13:38:48.261910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
standard 18379
36.8%
good 12260
24.5%
9805
19.6%
bad 9556
19.1%

Most occurring characters

ValueCountFrequency (%)
d 58574
25.0%
a 46314
19.7%
o 24520
10.5%
S 18379
 
7.8%
t 18379
 
7.8%
n 18379
 
7.8%
r 18379
 
7.8%
G 12260
 
5.2%
_ 9805
 
4.2%
B 9556
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 184545
78.7%
Uppercase Letter 40195
 
17.1%
Connector Punctuation 9805
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 58574
31.7%
a 46314
25.1%
o 24520
13.3%
t 18379
 
10.0%
n 18379
 
10.0%
r 18379
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
S 18379
45.7%
G 12260
30.5%
B 9556
23.8%
Connector Punctuation
ValueCountFrequency (%)
_ 9805
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 224740
95.8%
Common 9805
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 58574
26.1%
a 46314
20.6%
o 24520
10.9%
S 18379
 
8.2%
t 18379
 
8.2%
n 18379
 
8.2%
r 18379
 
8.2%
G 12260
 
5.5%
B 9556
 
4.3%
Common
ValueCountFrequency (%)
_ 9805
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 58574
25.0%
a 46314
19.7%
o 24520
10.5%
S 18379
 
7.8%
t 18379
 
7.8%
n 18379
 
7.8%
r 18379
 
7.8%
G 12260
 
5.2%
_ 9805
 
4.2%
B 9556
 
4.1%
Distinct12685
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:48.643749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.43302
Min length3

Characters and Unicode

Total characters321651
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique473 ?
Unique (%)0.9%

Sample

1st row809.98
2nd row809.98
3rd row809.98
4th row809.98
5th row605.03
ValueCountFrequency (%)
1109.03 12
 
< 0.1%
460.46 12
 
< 0.1%
1151.7 12
 
< 0.1%
1360.45 12
 
< 0.1%
255.82 8
 
< 0.1%
579.47 8
 
< 0.1%
248.84 8
 
< 0.1%
469.43 8
 
< 0.1%
2552.06 8
 
< 0.1%
604.77 8
 
< 0.1%
Other values (12193) 49904
99.8%
2023-10-21T13:38:49.263457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 50000
15.5%
1 41784
13.0%
2 31968
9.9%
3 29420
9.1%
4 29176
9.1%
5 24732
7.7%
6 24456
7.6%
8 24004
7.5%
7 23832
7.4%
9 23572
7.3%
Other values (2) 18707
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 271160
84.3%
Other Punctuation 50000
 
15.5%
Connector Punctuation 491
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 41784
15.4%
2 31968
11.8%
3 29420
10.8%
4 29176
10.8%
5 24732
9.1%
6 24456
9.0%
8 24004
8.9%
7 23832
8.8%
9 23572
8.7%
0 18216
6.7%
Other Punctuation
ValueCountFrequency (%)
. 50000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 491
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 321651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 50000
15.5%
1 41784
13.0%
2 31968
9.9%
3 29420
9.1%
4 29176
9.1%
5 24732
7.7%
6 24456
7.6%
8 24004
7.5%
7 23832
7.4%
9 23572
7.3%
Other values (2) 18707
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 50000
15.5%
1 41784
13.0%
2 31968
9.9%
3 29420
9.1%
4 29176
9.1%
5 24732
7.7%
6 24456
7.6%
8 24004
7.5%
7 23832
7.4%
9 23572
7.3%
Other values (2) 18707
 
5.8%

Credit_Utilization_Ratio
Real number (ℝ)

UNIQUE 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.279581
Minimum20.509652
Maximum48.540663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:49.760270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20.509652
5-th percentile24.274339
Q128.06104
median32.28039
Q336.468591
95-th percentile40.244882
Maximum48.540663
Range28.031011
Interquartile range (IQR)8.4075506

Descriptive statistics

Standard deviation5.1062377
Coefficient of variation (CV)0.15818786
Kurtosis-0.94942073
Mean32.279581
Median Absolute Deviation (MAD)4.2018629
Skewness0.037595743
Sum1613979.1
Variance26.073664
MonotonicityNot monotonic
2023-10-21T13:38:50.043765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.03040186 1
 
< 0.1%
24.9629253 1
 
< 0.1%
32.54665571 1
 
< 0.1%
35.6410219 1
 
< 0.1%
27.27736417 1
 
< 0.1%
25.13108037 1
 
< 0.1%
36.90353702 1
 
< 0.1%
36.16529856 1
 
< 0.1%
33.40214551 1
 
< 0.1%
40.05400789 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
ValueCountFrequency (%)
20.50965206 1
< 0.1%
20.62001732 1
< 0.1%
20.73922549 1
< 0.1%
20.80058685 1
< 0.1%
20.83922638 1
< 0.1%
20.91964798 1
< 0.1%
21.11966911 1
< 0.1%
21.14020193 1
< 0.1%
21.18158151 1
< 0.1%
21.18710526 1
< 0.1%
ValueCountFrequency (%)
48.54066309 1
< 0.1%
48.22871401 1
< 0.1%
48.15277749 1
< 0.1%
48.09645727 1
< 0.1%
48.06528066 1
< 0.1%
47.28898726 1
< 0.1%
47.23010359 1
< 0.1%
47.16317245 1
< 0.1%
46.97777638 1
< 0.1%
46.94753325 1
< 0.1%

Credit_History_Age
Text

MISSING 

Distinct399
Distinct (%)0.9%
Missing4470
Missing (%)8.9%
Memory size390.8 KiB
2023-10-21T13:38:50.306744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length21
Mean length20.975379
Min length20

Characters and Unicode

Total characters955009
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22 Years and 9 Months
2nd row22 Years and 10 Months
3rd row23 Years and 0 Months
4th row27 Years and 3 Months
5th row27 Years and 4 Months
ValueCountFrequency (%)
and 45530
20.0%
months 45530
20.0%
years 45530
20.0%
6 6000
 
2.6%
7 5955
 
2.6%
1 4948
 
2.2%
8 4885
 
2.1%
9 4857
 
2.1%
10 4766
 
2.1%
11 4651
 
2.0%
Other values (28) 54998
24.2%
2023-10-21T13:38:50.884565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
182120
19.1%
a 91060
9.5%
s 91060
9.5%
n 91060
9.5%
M 45530
 
4.8%
t 45530
 
4.8%
Y 45530
 
4.8%
e 45530
 
4.8%
r 45530
 
4.8%
d 45530
 
4.8%
Other values (12) 226529
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 546360
57.2%
Space Separator 182120
 
19.1%
Decimal Number 135469
 
14.2%
Uppercase Letter 91060
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36744
27.1%
2 22565
16.7%
3 13607
 
10.0%
0 12936
 
9.5%
6 9805
 
7.2%
7 9479
 
7.0%
8 8668
 
6.4%
9 8615
 
6.4%
4 6716
 
5.0%
5 6334
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
a 91060
16.7%
s 91060
16.7%
n 91060
16.7%
t 45530
8.3%
e 45530
8.3%
r 45530
8.3%
d 45530
8.3%
h 45530
8.3%
o 45530
8.3%
Uppercase Letter
ValueCountFrequency (%)
M 45530
50.0%
Y 45530
50.0%
Space Separator
ValueCountFrequency (%)
182120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 637420
66.7%
Common 317589
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
182120
57.3%
1 36744
 
11.6%
2 22565
 
7.1%
3 13607
 
4.3%
0 12936
 
4.1%
6 9805
 
3.1%
7 9479
 
3.0%
8 8668
 
2.7%
9 8615
 
2.7%
4 6716
 
2.1%
Latin
ValueCountFrequency (%)
a 91060
14.3%
s 91060
14.3%
n 91060
14.3%
M 45530
7.1%
t 45530
7.1%
Y 45530
7.1%
e 45530
7.1%
r 45530
7.1%
d 45530
7.1%
h 45530
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 955009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
182120
19.1%
a 91060
9.5%
s 91060
9.5%
n 91060
9.5%
M 45530
 
4.8%
t 45530
 
4.8%
Y 45530
 
4.8%
e 45530
 
4.8%
r 45530
 
4.8%
d 45530
 
4.8%
Other values (12) 226529
23.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Yes
26158 
No
17849 
NM
5993 

Length

Max length3
Median length3
Mean length2.52316
Min length2

Characters and Unicode

Total characters126158
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Yes 26158
52.3%
No 17849
35.7%
NM 5993
 
12.0%

Length

2023-10-21T13:38:51.117899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T13:38:51.276085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
yes 26158
52.3%
no 17849
35.7%
nm 5993
 
12.0%

Most occurring characters

ValueCountFrequency (%)
Y 26158
20.7%
e 26158
20.7%
s 26158
20.7%
N 23842
18.9%
o 17849
14.1%
M 5993
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70165
55.6%
Uppercase Letter 55993
44.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 26158
46.7%
N 23842
42.6%
M 5993
 
10.7%
Lowercase Letter
ValueCountFrequency (%)
e 26158
37.3%
s 26158
37.3%
o 17849
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 126158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 26158
20.7%
e 26158
20.7%
s 26158
20.7%
N 23842
18.9%
o 17849
14.1%
M 5993
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 26158
20.7%
e 26158
20.7%
s 26158
20.7%
N 23842
18.9%
o 17849
14.1%
M 5993
 
4.8%

Total_EMI_per_month
Real number (ℝ)

ZEROS 

Distinct13144
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1491.3043
Minimum0
Maximum82398
Zeros5002
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-10-21T13:38:51.543099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132.222388
median74.733349
Q3176.15749
95-th percentile683.41153
Maximum82398
Range82398
Interquartile range (IQR)143.9351

Descriptive statistics

Standard deviation8595.6479
Coefficient of variation (CV)5.7638457
Kurtosis49.802555
Mean1491.3043
Median Absolute Deviation (MAD)55.075845
Skewness6.9462753
Sum74565215
Variance73885163
MonotonicityNot monotonic
2023-10-21T13:38:51.772583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5002
 
10.0%
49.57494921 4
 
< 0.1%
16.94190267 4
 
< 0.1%
420.1993667 4
 
< 0.1%
550.6793937 4
 
< 0.1%
109.8118218 4
 
< 0.1%
161.4055427 4
 
< 0.1%
56.62512462 4
 
< 0.1%
64.2020639 4
 
< 0.1%
244.3542363 4
 
< 0.1%
Other values (13134) 44962
89.9%
ValueCountFrequency (%)
0 5002
10.0%
4.462837467 4
 
< 0.1%
4.713183572 4
 
< 0.1%
4.865689677 4
 
< 0.1%
4.916138542 4
 
< 0.1%
5.138484696 4
 
< 0.1%
5.218466359 4
 
< 0.1%
5.24927327 4
 
< 0.1%
5.262291048 4
 
< 0.1%
5.351086151 4
 
< 0.1%
ValueCountFrequency (%)
82398 1
< 0.1%
82347 1
< 0.1%
82316 1
< 0.1%
82248 1
< 0.1%
82235 1
< 0.1%
82225 1
< 0.1%
82091 1
< 0.1%
82071 1
< 0.1%
82023 1
< 0.1%
82016 1
< 0.1%
Distinct45450
Distinct (%)95.2%
Missing2271
Missing (%)4.5%
Memory size390.8 KiB
2023-10-21T13:38:52.116060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length17
Mean length16.957112
Min length3

Characters and Unicode

Total characters809346
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45448 ?
Unique (%)95.2%

Sample

1st row236.64268203272135
2nd row21.465380264657146
3rd row148.23393788500925
4th row39.08251089460281
5th row39.684018417945296
ValueCountFrequency (%)
10000 2175
 
4.6%
0.0 106
 
0.2%
183.234270604515 1
 
< 0.1%
52.808230662619934 1
 
< 0.1%
160.24431272401932 1
 
< 0.1%
841.2322359154716 1
 
< 0.1%
148.23393788500925 1
 
< 0.1%
39.08251089460281 1
 
< 0.1%
39.684018417945296 1
 
< 0.1%
251.62736875017606 1
 
< 0.1%
Other values (45440) 45440
95.2%
2023-10-21T13:38:52.677186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 86365
10.7%
2 77857
9.6%
4 76027
9.4%
3 75728
9.4%
0 75606
9.3%
6 74699
9.2%
5 74363
9.2%
8 72540
9.0%
7 72532
9.0%
9 69375
8.6%
Other values (2) 54254
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 755092
93.3%
Other Punctuation 45554
 
5.6%
Connector Punctuation 8700
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 86365
11.4%
2 77857
10.3%
4 76027
10.1%
3 75728
10.0%
0 75606
10.0%
6 74699
9.9%
5 74363
9.8%
8 72540
9.6%
7 72532
9.6%
9 69375
9.2%
Other Punctuation
ValueCountFrequency (%)
. 45554
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 809346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 86365
10.7%
2 77857
9.6%
4 76027
9.4%
3 75728
9.4%
0 75606
9.3%
6 74699
9.2%
5 74363
9.2%
8 72540
9.0%
7 72532
9.0%
9 69375
8.6%
Other values (2) 54254
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 809346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 86365
10.7%
2 77857
9.6%
4 76027
9.4%
3 75728
9.4%
0 75606
9.3%
6 74699
9.2%
5 74363
9.2%
8 72540
9.0%
7 72532
9.0%
9 69375
8.6%
Other values (2) 54254
6.7%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Low_spent_Small_value_payments
12694 
High_spent_Medium_value_payments
8922 
High_spent_Large_value_payments
6844 
Low_spent_Medium_value_payments
6837 
High_spent_Small_value_payments
5651 
Other values (2)
9052 

Length

Max length32
Median length31
Mean length28.91952
Min length6

Characters and Unicode

Total characters1445976
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow_spent_Small_value_payments
2nd rowHigh_spent_Medium_value_payments
3rd rowLow_spent_Medium_value_payments
4th rowHigh_spent_Medium_value_payments
5th rowHigh_spent_Large_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments 12694
25.4%
High_spent_Medium_value_payments 8922
17.8%
High_spent_Large_value_payments 6844
13.7%
Low_spent_Medium_value_payments 6837
13.7%
High_spent_Small_value_payments 5651
11.3%
Low_spent_Large_value_payments 5252
10.5%
!@9#%8 3800
 
7.6%

Length

2023-10-21T13:38:52.916148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T13:38:53.097731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments 12694
25.4%
high_spent_medium_value_payments 8922
17.8%
high_spent_large_value_payments 6844
13.7%
low_spent_medium_value_payments 6837
13.7%
high_spent_small_value_payments 5651
11.3%
low_spent_large_value_payments 5252
10.5%
9#%8 3800
 
7.6%

Most occurring characters

ValueCountFrequency (%)
_ 184800
12.8%
e 166455
11.5%
a 122841
 
8.5%
s 92400
 
6.4%
p 92400
 
6.4%
n 92400
 
6.4%
t 92400
 
6.4%
l 82890
 
5.7%
m 80304
 
5.6%
u 61959
 
4.3%
Other values (19) 377127
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1145976
79.3%
Connector Punctuation 184800
 
12.8%
Uppercase Letter 92400
 
6.4%
Other Punctuation 15200
 
1.1%
Decimal Number 7600
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 166455
14.5%
a 122841
10.7%
s 92400
8.1%
p 92400
8.1%
n 92400
8.1%
t 92400
8.1%
l 82890
 
7.2%
m 80304
 
7.0%
u 61959
 
5.4%
v 46200
 
4.0%
Other values (8) 215727
18.8%
Uppercase Letter
ValueCountFrequency (%)
L 36879
39.9%
H 21417
23.2%
S 18345
19.9%
M 15759
17.1%
Other Punctuation
ValueCountFrequency (%)
! 3800
25.0%
@ 3800
25.0%
# 3800
25.0%
% 3800
25.0%
Decimal Number
ValueCountFrequency (%)
9 3800
50.0%
8 3800
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 184800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1238376
85.6%
Common 207600
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 166455
13.4%
a 122841
 
9.9%
s 92400
 
7.5%
p 92400
 
7.5%
n 92400
 
7.5%
t 92400
 
7.5%
l 82890
 
6.7%
m 80304
 
6.5%
u 61959
 
5.0%
v 46200
 
3.7%
Other values (12) 308127
24.9%
Common
ValueCountFrequency (%)
_ 184800
89.0%
! 3800
 
1.8%
@ 3800
 
1.8%
9 3800
 
1.8%
# 3800
 
1.8%
% 3800
 
1.8%
8 3800
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1445976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 184800
12.8%
e 166455
11.5%
a 122841
 
8.5%
s 92400
 
6.4%
p 92400
 
6.4%
n 92400
 
6.4%
t 92400
 
6.4%
l 82890
 
5.7%
m 80304
 
5.6%
u 61959
 
4.3%
Other values (19) 377127
26.1%

Monthly_Balance
Text

MISSING 

Distinct49433
Distinct (%)> 99.9%
Missing562
Missing (%)1.1%
Memory size390.8 KiB
2023-10-21T13:38:53.547556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length17
Mean length17.342348
Min length13

Characters and Unicode

Total characters857371
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49432 ?
Unique (%)> 99.9%

Sample

1st row186.26670208571772
2nd row361.44400385378196
3rd row264.67544623342997
4th row343.82687322383634
5th row485.2984336755923
ValueCountFrequency (%)
333333333333333333333333333 6
 
< 0.1%
34.51483452315301 1
 
< 0.1%
486.1759805984733 1
 
< 0.1%
715.4219008245058 1
 
< 0.1%
264.67544623342997 1
 
< 0.1%
343.82687322383634 1
 
< 0.1%
485.2984336755923 1
 
< 0.1%
303.3550833433617 1
 
< 0.1%
452.30230675990265 1
 
< 0.1%
421.44796447960783 1
 
< 0.1%
Other values (49423) 49423
> 99.9%
2023-10-21T13:38:54.249930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 90947
10.6%
2 90118
10.5%
4 84677
9.9%
5 81313
9.5%
6 80808
9.4%
7 78501
9.2%
1 77986
9.1%
8 77928
9.1%
9 74663
8.7%
0 70968
8.3%
Other values (3) 49462
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 807909
94.2%
Other Punctuation 49432
 
5.8%
Connector Punctuation 24
 
< 0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 90947
11.3%
2 90118
11.2%
4 84677
10.5%
5 81313
10.1%
6 80808
10.0%
7 78501
9.7%
1 77986
9.7%
8 77928
9.6%
9 74663
9.2%
0 70968
8.8%
Other Punctuation
ValueCountFrequency (%)
. 49432
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 857371
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 90947
10.6%
2 90118
10.5%
4 84677
9.9%
5 81313
9.5%
6 80808
9.4%
7 78501
9.2%
1 77986
9.1%
8 77928
9.1%
9 74663
8.7%
0 70968
8.3%
Other values (3) 49462
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 857371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 90947
10.6%
2 90118
10.5%
4 84677
9.9%
5 81313
9.5%
6 80808
9.4%
7 78501
9.2%
1 77986
9.1%
8 77928
9.1%
9 74663
8.7%
0 70968
8.3%
Other values (3) 49462
5.8%

Interactions

2023-10-21T13:38:30.152261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:18.972451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:20.407029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:21.884353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:23.342900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:24.896716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:26.558901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:28.347588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:30.350513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:19.162543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:20.580946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:22.068116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:23.524751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:25.128581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:26.781380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:28.524271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:30.533523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:19.341426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:20.774266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:22.243060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:23.727492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:25.348982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:27.006949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:28.752292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:30.702853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:19.516490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:20.998683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:22.500771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:23.914194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:25.529696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:27.208407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:28.941677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:30.903628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:19.688574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:21.183653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:22.672203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:24.094143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:25.743453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:27.406617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:29.118831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:31.077672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:19.884176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:21.366594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:22.852609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:24.351595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:25.947587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:27.597355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:29.352068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:31.294056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:20.049744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:21.547149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:23.015791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:24.547992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:26.128082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:27.996652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:29.744952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:31.462502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:20.243015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:21.717195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:23.179210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:24.708242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:26.324245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:28.177789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-21T13:38:29.985781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-21T13:38:54.447053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Monthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateDelay_from_due_dateNum_Credit_InquiriesCredit_Utilization_RatioTotal_EMI_per_monthMonthOccupationCredit_MixPayment_of_Min_AmountPayment_Behaviour
Monthly_Inhand_Salary1.000-0.261-0.190-0.286-0.239-0.2610.1280.4430.0000.0250.2130.2290.165
Num_Bank_Accounts-0.2611.0000.4010.5560.5560.483-0.0660.0940.0060.0000.0070.0110.000
Num_Credit_Card-0.1900.4011.0000.4260.4220.389-0.0470.0910.0000.0000.0000.0000.009
Interest_Rate-0.2860.5560.4261.0000.5500.571-0.0590.1220.0000.0040.0100.0060.005
Delay_from_due_date-0.2390.5560.4220.5501.0000.490-0.0640.1210.0000.0230.4250.3570.040
Num_Credit_Inquiries-0.2610.4830.3890.5710.4901.000-0.0650.1480.0060.0000.0050.0030.000
Credit_Utilization_Ratio0.128-0.066-0.047-0.059-0.064-0.0651.0000.0060.0000.0000.0660.0720.070
Total_EMI_per_month0.4430.0940.0910.1220.1210.1480.0061.0000.0100.0020.0000.0110.000
Month0.0000.0060.0000.0000.0000.0060.0000.0101.0000.0000.0020.0000.000
Occupation0.0250.0000.0000.0040.0230.0000.0000.0020.0001.0000.0220.0130.006
Credit_Mix0.2130.0070.0000.0100.4250.0050.0660.0000.0020.0221.0000.4890.065
Payment_of_Min_Amount0.2290.0110.0000.0060.3570.0030.0720.0110.0000.0130.4891.0000.074
Payment_Behaviour0.1650.0000.0090.0050.0400.0000.0700.0000.0000.0060.0650.0741.000

Missing values

2023-10-21T13:38:31.769302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-21T13:38:32.452607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-21T13:38:33.196044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDCustomer_IDMonthNameAgeSSNOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_Balance
00x160aCUS_0xd40SeptemberAaron Maashoh23821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan3711.272022.0Good809.9835.03040222 Years and 9 MonthsNo49.574949236.64268203272135Low_spent_Small_value_payments186.26670208571772
10x160bCUS_0xd40OctoberAaron Maashoh24821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan3913.274.0Good809.9833.05311422 Years and 10 MonthsNo49.57494921.465380264657146High_spent_Medium_value_payments361.44400385378196
20x160cCUS_0xd40NovemberAaron Maashoh24821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan-1412.274.0Good809.9833.811894NaNNo49.574949148.23393788500925Low_spent_Medium_value_payments264.67544623342997
30x160dCUS_0xd40DecemberAaron Maashoh24_821-00-0265Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan4511.274.0Good809.9832.43055923 Years and 0 MonthsNo49.57494939.08251089460281High_spent_Medium_value_payments343.82687322383634
40x1616CUS_0x21b1SeptemberRick Rothackerj28004-07-5839_______34847.843037.9866672461Credit-Builder Loan315.425.0Good605.0325.92682227 Years and 3 MonthsNo18.81621539.684018417945296High_spent_Large_value_payments485.2984336755923
50x1617CUS_0x21b1OctoberRick Rothackerj28#F%$D@*&8Teacher34847.843037.9866672461Credit-Builder Loan335.425.0Good605.0330.11660027 Years and 4 MonthsNo18.816215251.62736875017606Low_spent_Large_value_payments303.3550833433617
60x1618CUS_0x21b1NovemberRick Rothackerj28004-07-5839Teacher34847.843037.9866672461Credit-Builder Loan3NaN5.425.0_605.0330.99642427 Years and 5 MonthsNo18.81621572.68014533363515High_spent_Large_value_payments452.30230675990265
70x1619CUS_0x21b1DecemberRick Rothackerj28004-07-5839Teacher34847.843037.9866672461Credit-Builder Loan32_7.425.0_605.0333.87516727 Years and 6 MonthsNo18.816215153.53448761392985!@9#%8421.44796447960783
80x1622CUS_0x2dbcSeptemberLangep35486-85-3974Engineer143162.64NaN1583Auto Loan, Auto Loan, and Not Specified819427.13.0Good1303.0135.22970718 Years and 5 MonthsNo246.992319397.50365354404653Low_spent_Medium_value_payments854.2260270022115
90x1623CUS_0x2dbcOctoberLangep35486-85-3974Engineer143162.6412187.2200001583Auto Loan, Auto Loan, and Not Specified632.13.0Good1303.0135.68583618 Years and 6 MonthsNo246.992319453.6151305781054Low_spent_Large_value_payments788.1145499681528
IDCustomer_IDMonthNameAgeSSNOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_Balance
499900x25fd8CUS_0xaf61NovemberChris Wickhamm50133-16-7738Writer37188.13097.0083331442523Home Equity Loan, Mortgage Loan, and Student Loan7125.383.0Good620.6425.70841430 Years and 7 MonthsNo84.205949183.3656280777276Low_spent_Large_value_payments312.1292558307615
499910x25fd9CUS_0xaf61DecemberChris Wickhamm50_133-16-7738Writer37188.13097.0083331453Home Equity Loan, Mortgage Loan, and Student Loan3125.383.0_620.6436.49838330 Years and 8 MonthsNo33013.000000238.3993828976901Low_spent_Large_value_payments257.095501010799
499920x25fe2CUS_0x8600SeptemberSarah McBridec29031-35-0942Architect20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan332518.319.0Bad3571.732.3912886 Years and 4 MonthsYes60.964772107.21074164760236Low_spent_Small_value_payments314.8151526456419
499930x25fe3CUS_0x8600OctoberSarah McBridec29031-35-0942Architect20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan332518.3112.0Bad3571.737.5285116 Years and 5 MonthsYes60.96477271.79442082882734Low_spent_Small_value_payments350.23147346441687
499940x25fe4CUS_0x8600NovemberSarah McBridec29031-35-0942_______20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan332218.3112.0Bad3571.727.0278126 Years and 6 MonthsYes60.96477250.84684680498023High_spent_Small_value_payments341.179047488264
499950x25fe5CUS_0x8600DecemberSarah McBridec4975031-35-0942Architect20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan332518.3112.0_3571.734.780553NaNYes60.964772146.48632477751087Low_spent_Small_value_payments275.53956951573343
499960x25feeCUS_0x942cSeptemberNicks25078-73-5990Mechanic39628.99NaN4672_Auto Loan, and Student Loan20NaN11.57.0Good502.3827.75852231 Years and 11 MonthsNM35.104023181.44299902757518Low_spent_Small_value_payments409.39456169535066
499970x25fefCUS_0x942cOctoberNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan23513.57.0Good502.3836.85854232 Years and 0 MonthsNo35.104023__10000__Low_spent_Large_value_payments349.7263321025098
499980x25ff0CUS_0x942cNovemberNicks25078-73-5990Mechanic39628.99NaN4672_Auto Loan, and Student Loan216_11.57.0Good502.3839.13984032 Years and 1 MonthsNo35.10402397.59857973344877High_spent_Small_value_payments463.23898098947717
499990x25ff1CUS_0x942cDecemberNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan22511.57.0_502.3834.10853032 Years and 2 MonthsNo35.104023220.45787812168732Low_spent_Medium_value_payments360.37968260123847